A method for controlling copper wire arrangement in a vehicle-mounted cable weaving process

Abstract

The application relates to a copper wire arrangement control method in a vehicle-mounted cable weaving process in the field of intelligent manufacturing, and the method comprises the following steps: executing a copper wire arrangement optimization algorithm according to a magnetic field control strategy, adjusting the magnetic field gradient distribution of each weaving point in real time, making the copper wire orderly arranged according to the predetermined weaving angle and density requirements, and obtaining a uniform and stable weaving density distribution state; continuously evaluating the weaving density distribution state through an optical detection system, collecting high-resolution image data of the weaving layer surface, if the weaving angle deviation or the uneven density area is detected, generating quality abnormal identification information, and obtaining a weaving quality real-time evaluation result; continuously monitoring the weaving process by using the corrected process parameter combination, tracking the copper wire arrangement state change trend through a real-time monitoring system, if the change trend deviates from the preset track, automatically starting an intelligent weaving adjustment program, and obtaining a stable and controllable weaving process state.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing, specifically to intelligent manufacturing process control technology, and more particularly to a method for controlling the arrangement of copper wires during the braiding process of vehicle-mounted cables. Background Technology

[0002] The rapid increase in automotive electronics has presented unprecedented electromagnetic challenges to in-vehicle cabling systems, making efficient electromagnetic shielding technology a core element for ensuring the stable operation of automotive electronic systems. With the widespread application of technologies such as autonomous driving and vehicle-to-everything (V2X), the requirements for cable anti-interference capabilities and mechanical reliability are becoming increasingly stringent. Traditional shielding braiding processes mainly rely on mechanical tension to control the arrangement of copper wires, resulting in uneven braiding density and limited shielding effectiveness.

[0003] Existing methods typically improve performance by increasing shielding layer thickness or material density, but this not only increases cable weight and cost but may also affect cable flexibility. Furthermore, traditional quality inspection methods often rely on sampling, making it difficult to achieve comprehensive defect identification. The fundamental challenge facing current shielding braiding processes lies in precisely controlling the spatial arrangement of the copper wires. Fixed-parameter magnetic field control systems cannot adapt to the arrangement requirements of copper wires with different diameters and materials, leading to braiding angle deviations and density instability. This inaccuracy in arrangement control directly affects the continuity and uniformity of the shielding layer, thus limiting the improvement of overall shielding effectiveness. More critically, braiding systems lacking real-time parameter adjustment capabilities require frequent manual intervention and parameter resets when facing diverse product demands, increasing operational complexity and easily introducing human error, affecting product quality consistency.

[0004] Therefore, how to construct an intelligent braiding system that can automatically adjust magnetic field parameters according to different process conditions, and achieve precise control of the copper wire arrangement and simplified process operation, has become a key issue in improving the shielding performance and production efficiency of automotive wiring harnesses. Summary of the Invention

[0005] This invention provides a method for controlling the arrangement of copper wires during the braiding process of vehicle-mounted cables, comprising the following steps:

[0006] Real-time spatial state data of copper wires during the braiding process of vehicle-mounted cables are obtained. The three-dimensional coordinate information and motion trajectory parameters of copper wires in the braiding area are collected by a high-precision sensor array. A basic process condition database is established based on the material characteristics and wire diameter specifications of copper wires to obtain the initial feature vector of the copper wire arrangement state.

[0007] An adaptive magnetic field adjustment algorithm is used to analyze the feature vector of the copper wire arrangement state. If the deviation of the copper wire spacing exceeds the preset threshold range, the magnetic field strength distribution parameters are dynamically adjusted. The copper wire is precisely positioned during the weaving process by combining multiple magnetic field generators to determine the optimal magnetic field control strategy.

[0008] According to the magnetic field control strategy, the copper wire arrangement optimization algorithm is executed. By adjusting the magnetic field gradient distribution of each braiding point in real time, the copper wires are arranged in an orderly manner according to the predetermined braiding angle and density requirements, so as to obtain a uniform and stable braiding density distribution.

[0009] The weaving density distribution is continuously evaluated by an optical detection system, and high-resolution image data of the surface of the weaving layer is acquired. If weaving angle deviation or density uneven area is detected, quality anomaly identification information is generated to obtain real-time weaving quality evaluation results.

[0010] The parameter adjustment mechanism is triggered based on the real-time evaluation results of the weaving quality. If the quality evaluation shows that the shielding effectiveness index is lower than the standard requirements, the magnetic field control parameters and weaving process conditions are adjusted retrospectively. The accuracy of the copper wire spatial arrangement is optimized through closed-loop feedback control, and the corrected combination of process parameters is determined.

[0011] The braiding process is continuously monitored using the modified process parameter combination. The copper wire arrangement state is tracked through a real-time monitoring system. If the change trend deviates from the preset track, the intelligent braiding adjustment program is automatically started to obtain a stable and controllable braiding process state.

[0012] Based on the braiding process status data, a shielding effectiveness prediction model is established. By analyzing the correlation between braiding density and shielding performance, if the predicted shielding effectiveness meets the design requirements, a qualified vehicle cable shielding braiding product is output, resulting in a high-quality cable that meets the anti-interference requirements of automotive electronic systems.

[0013] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0014] This invention discloses a method for controlling the arrangement of copper wires during the braiding process of automotive cables. A high-precision sensor array is used to collect the three-dimensional coordinate information of the copper wires in real time, establishing a basic process condition database to obtain the initial feature vector of the copper wire arrangement state. An adaptive magnetic field adjustment algorithm is used to analyze the feature vector and dynamically adjust the magnetic field strength distribution parameters to achieve precise copper wire positioning. A copper wire arrangement optimization algorithm is executed to adjust the magnetic field gradient distribution at each braiding point, ensuring the copper wires are arranged in an orderly manner according to predetermined angle and density requirements. The braiding density distribution state is evaluated through an optical detection system, generating quality anomaly identification information. Based on the evaluation results, a parameter adjustment mechanism is triggered to optimize the spatial arrangement accuracy of the copper wires. A shielding effectiveness prediction model is established to analyze the correlation between braiding density and shielding performance, ultimately outputting a high-quality cable that meets the anti-interference requirements of automotive electronic systems. This invention achieves precise control and quality optimization of the automotive cable braiding process, improving shielding effectiveness and product reliability. Detailed Implementation

[0015] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] This embodiment of a method for controlling the arrangement of copper wires during the braiding process of vehicle-mounted cables may specifically include:

[0017] S101. Obtain real-time spatial state data of copper wires during the braiding process of vehicle-mounted cables. Collect three-dimensional coordinate information and motion trajectory parameters of copper wires in the braiding area through a high-precision sensor array. Establish a basic process condition database based on the material characteristics and wire diameter specifications of copper wires to obtain the initial feature vector of copper wire arrangement state.

[0018] The process involves acquiring raw 3D coordinate data of copper wires from a sensor array, filtering out noise interference through a data preprocessing module to obtain purified spatial position information. Based on this purified spatial position information, the copper wire trajectory parameters are calculated. A trajectory analysis algorithm is used to extract the velocity and acceleration vectors of the copper wires within the weaving area, determining the characteristics of the copper wire's motion state. If the matching result between the copper wire motion state characteristics and preset wire diameter specifications exceeds a preset threshold range, a trajectory correction mechanism is triggered to obtain standardized motion trajectory data. Using this standardized motion trajectory data and copper wire material properties, a feature extraction algorithm generates a multi-dimensional feature vector of the copper wire arrangement state, resulting in a state description matrix for the weaving process. Based on the distribution of feature vectors in the state description matrix, the distance and angle deviation parameters between copper wires are calculated. If these parameters exceed the allowable range of the process parameters, the control system sends adjustment commands to the weaving equipment. The process parameter database is updated using the corrected copper wire arrangement state data, and the feature vector extraction model is optimized using incremental learning. The distance and angle deviation parameters between copper wires are recalculated based on the updated process parameter database until the parameters meet the process requirements.

[0019] Specifically, during the braiding process of the vehicle-mounted cable, a laser displacement sensor array is first used to acquire the three-dimensional coordinate data of the copper wire in real time at a sampling frequency of 1000Hz. The sensor accuracy reaches 0.01mm, covering a braiding area of ​​200mm×150mm. The system uses a Kalman filter algorithm to reduce noise in the acquired coordinate points, with the filter parameters Q set to 0.001 and R set to 0.01 to ensure the accuracy of the copper wire position data. Based on continuous coordinate points, the trajectory of the copper wire is calculated, and the velocity vector V(t)=(x(t+1)-x(t)) / Δt is obtained through a differential algorithm, where Δt is 0.001 seconds. For tin-plated copper wire with a diameter of 0.2mm, a material property database is established, including an elastic modulus of 110GPa, Poisson's ratio of 0.34, and a density of 8.96g / cm³. 3 Based on parameters such as wire diameter, the tensile strength of a single copper wire was calculated to be 220 MPa. The system uses principal component analysis to extract feature vectors based on the spatial distribution of the copper wires at the weaving points. A five-dimensional feature vector [x,y,z,θ,F] is formed by combining the position coordinates (x,y,z), bending angle θ, and tension value F of each copper wire. This vector is then standardized to obtain an initial feature vector matrix, providing a data foundation for subsequent weaving quality control. The entire data acquisition and processing process achieves millisecond-level response, ensuring real-time monitoring and quality assurance of the weaving process.

[0020] S102. The adaptive magnetic field adjustment algorithm is used to analyze the feature vector of the copper wire arrangement state. If the deviation of the copper wire spacing exceeds the preset threshold range, the magnetic field strength distribution parameters are dynamically adjusted. The copper wire is precisely positioned during the weaving process by combining multiple magnetic field generators to determine the optimal magnetic field control strategy.

[0021] The process involves acquiring position coordinate data during copper wire weaving, collecting spatial distribution information of the copper wire arrangement using an image sensor to obtain an original position data matrix, calculating the spacing between adjacent copper wires based on the original position data matrix, and calculating the spacing deviation using the Euclidean distance formula to obtain the spacing deviation distribution. The spacing deviation distribution is then analyzed using a support vector machine algorithm to extract feature vectors of the copper wire arrangement. If the deviation value in the feature vector exceeds a preset threshold range, the copper wire arrangement is judged to be abnormal, and abnormal area identification information is obtained. This abnormal area identification information is used to drive a magnetic field strength adjustment module to calculate a magnetic field strength compensation value based on the degree of deviation. The target magnetic field strength parameter is received by a multi-point magnetic field generator group. The Kalman filter algorithm is used to predict the trajectory of the copper wire. If the deviation between the predicted trajectory and the standard trajectory exceeds the tolerance range, the magnetic field output power of each generator is adjusted in real time to determine the precise positioning control command. According to the precise positioning control command, multiple magnetic field generators are driven synchronously to form a spatial magnetic field distribution gradient through the magnetic field superposition effect, thereby obtaining the force balance state of the copper wire in the braiding area. A feedback control mechanism is used to monitor the deviation between the actual position of the copper wire and the target position. If the deviation continues to decrease and stabilizes within the preset range, the current magnetic field control strategy is determined to be the optimal configuration scheme.

[0022] Specifically, in the adaptive magnetic field adjustment algorithm, the real-time spatial coordinates of the copper wire arrangement are first acquired using a high-precision laser scanner. The sampling frequency is set to 1000Hz, and the scanning accuracy reaches 0.01mm, generating a dataset containing 12-dimensional feature vectors including positional deviation and angular offset. Principal component analysis is used to reduce the dimensionality of the feature vectors, retaining the three principal components with a contribution rate exceeding 95% as core features. When the copper wire spacing deviation exceeds a preset threshold of ±0.05mm, a fuzzy PID-based control algorithm is triggered, where the proportional coefficient Kp is initially set to 1.2, the integral time Ti = 0.5s, and the derivative time Td = 0.1s. The deviation is quantified into five levels using a membership function. Magnetic field strength adjustment employs a fusion method of finite element simulation and measured data. A multiphysics model containing eight electromagnets is established in COMSOL with a mesh size of 0.5mm. The initial magnetic field distribution is obtained by solving the Maxwell equations. Then, the simulation data is fitted with the measured data from the Hall sensor using the least squares method, with the residual controlled within 2%. The optimal control strategy is implemented using a genetic algorithm with a population size of 50, a crossover probability of 0.8, and a mutation probability of 0.05. After 100 generations of iterations, the optimal current combination for each electromagnet is output. For example, the current of electromagnet 1 is adjusted to 3.2A, and that of electromagnet 2 is adjusted to 2.7A, ultimately improving the copper wire positioning accuracy to ±0.02mm. The entire process communicates with the host computer in real time via the OPC-UA protocol, with a data update cycle of 10ms, forming a closed-loop control.

[0023] S103. Execute the copper wire arrangement optimization algorithm according to the magnetic field control strategy. By adjusting the magnetic field gradient distribution of each braiding point in real time, the copper wires are arranged in an orderly manner according to the predetermined braiding angle and density requirements, so as to obtain a uniform and stable braiding density distribution state.

[0024] The spatial coordinates and copper wire material properties of the braiding points are acquired. Magnetic field strength data for each braiding point are collected using a sensor array to obtain an initial magnetic field distribution matrix. The magnetic field gradient difference between each braiding point is calculated based on this initial matrix. The gradient distribution parameters are determined using a gradient calculation formula, where G represents the magnetic field gradient, B represents the magnetic field strength, and r represents the spatial distance, resulting in a gradient distribution feature vector. If any braiding point has a gradient difference exceeding a preset threshold in the gradient distribution feature vector, the magnetic field control module is activated to adjust the magnetic field strength at the abnormal point. This adjustment is achieved by changing the local magnetic field distribution through coil current adjustment, resulting in the adjusted magnetic field distribution state. A genetic algorithm is used to optimize the adjusted magnetic field distribution state, using the minimization of the braiding angle deviation as the fitness function. Selection, crossover, and mutation operations are used to optimize the magnetic field parameter configuration and determine the optimal combination of magnetic field control parameters. Based on this optimal combination of magnetic field control parameters, the magnetic field generator at each braiding point is driven to monitor the displacement trajectory and angle changes of the copper wire under the influence of the magnetic field in real time. The actual arrangement state data of the copper wire is obtained through a position feedback sensor. If the actual arrangement data of the copper wires shows an uneven weaving density distribution, a PID control algorithm is used to adjust the magnetic field gradient distribution. The magnetic field control signal is corrected through proportional, integral, and derivative components to obtain a stable weaving density distribution. The weaving density distribution result is then evaluated using a density detection module, which calculates the density uniformity index and stability coefficient, generating a weaving quality evaluation report and optimization parameter suggestions.

[0025] Specifically, the system first collects real-time magnetic field strength data at various points within the weaving area using a three-dimensional magnetic field sensor array. The sensor spacing is set to 2.5 mm, the sampling frequency is 1000 Hz, and the obtained magnetic field strength ranges from 0.8 to 1.2 Tesla. Based on the collected magnetic field data, the system uses a gradient descent optimization algorithm to calculate the ideal magnetic field gradient value for each weaving point. The algorithm has a learning rate of 0.01, 500 iterations, and the objective function is to minimize the sum of squares of the weaving density deviation. When the magnetic field gradient at a weaving point deviates from the target value by more than 0.05 Tesla per millimeter, the control system immediately activates the magnetic field adjustment mechanism. This mechanism corrects the magnetic field distribution by adjusting the current intensity of the electromagnetic coil, achieving a current adjustment accuracy of 0.1 Amperes. The system employs a PID control algorithm to achieve precise magnetic field adjustment, with a proportional coefficient of 1.2, an integral coefficient of 0.8, and a derivative coefficient of 0.3, ensuring the stability and response speed of the magnetic field adjustment. After the magnetic field gradient reaches the preset range, the copper wires are arranged at a 45-degree braiding angle under the action of the magnetic field force. The braiding density is controlled at 16 copper wires per square centimeter. Through continuous monitoring and feedback adjustment, the system controls the uniformity deviation of the braiding density within ±2%, and finally achieves a high-precision orderly arrangement and stable density distribution of the copper wire braids.

[0026] S104. The weaving density distribution is continuously evaluated by an optical detection system. High-resolution image data of the surface of the weaving layer is collected. If a weaving angle deviation or a density uneven area is detected, quality anomaly identification information is generated to obtain real-time evaluation results of the weaving quality.

[0027] The process involves acquiring surface image data of the braided layer using an optical detection device, with the acquisition frequency dynamically adjusted based on the braiding speed. Preprocessing is performed on the image data; if the image quality falls below a preset threshold, it is re-acquired to obtain a braided structure image. Fiber arrangement feature information, including fiber direction and interlacing point positions, is extracted from the braided structure image. An edge detection algorithm is used to analyze this feature information, determining braiding angle parameters and density distribution data. A standard angle range model is established for the braiding angle parameters; if the measured angle deviation exceeds the standard range, it is marked as an angle anomaly region. Density distribution data is analyzed using density statistics methods to calculate the difference between local density values ​​and the overall average density; if the difference exceeds a preset tolerance range, it is identified as a density non-uniform region. A convolutional neural network is used to comprehensively analyze angle anomaly regions and density non-uniform regions, generating quality anomaly identification information and obtaining a braiding quality assessment result. A quality trend database is established based on the braiding quality assessment result, recording anomaly types and location distribution information to form a braiding quality monitoring file.

[0028] Specifically, the optical inspection system first acquires images of the woven layer surface using a high-resolution CCD camera at a resolution of 2048×2048 pixels, with a sampling frequency set to 30 frames per second to ensure continuous monitoring. The system preprocesses the images using an improved Canny edge detection algorithm, setting a high threshold of 150 and a low threshold of 50 to extract the edge features of the woven fibers. Then, it uses the Hough transform algorithm to identify the weaving angle, determining the deviation value by calculating the angle between adjacent fiber bundles. When a weaving angle deviates from the standard value of ±45° by more than 3°, the system automatically triggers an angle anomaly flag. The density detection module uses a texture analysis algorithm based on the gray-level co-occurrence matrix to divide the image into 20×20 pixel detection units, calculating the fiber density value within each unit. The standard density range is set to 16-20 fibers per square centimeter. The system compares the density differences between adjacent detection units, identifying areas of uneven density when the density change rate exceeds 15%. The quality assessment algorithm calculates a comprehensive quality index by combining an angle deviation weight of 0.6 and a density uniformity weight of 0.4. When the index is below 0.85, a red abnormality mark is generated; when it is between 0.85 and 0.95, a yellow warning mark is displayed; and when it is above 0.95, a green qualified mark is displayed, thus realizing real-time quantitative assessment and visual feedback of knitting quality.

[0029] S105. Based on the real-time evaluation result of the weaving quality, a parameter adjustment mechanism is triggered. If the quality evaluation shows that the shielding effectiveness index is lower than the standard requirement, the magnetic field control parameters and weaving process conditions are adjusted retrospectively. The spatial arrangement accuracy of the copper wires is optimized through closed-loop feedback control, and the corrected combination of process parameters is determined.

[0030] Real-time shielding effectiveness data during the copper wire braiding process is acquired. Electromagnetic shielding strength, conductivity continuity, and braiding density parameters are collected via a sensor array to obtain the current braiding effectiveness index value. The effectiveness index value is compared and analyzed against a preset shielding effectiveness threshold standard. If the real-time effectiveness index is lower than the standard threshold, a quality anomaly indicator is triggered. A parameter backtracking mechanism is used to analyze the process factors corresponding to the quality anomaly indicator. Historical data correlation analysis identifies the deviation degree of magnetic field strength, braiding tension, and wire diameter spacing, determining the parameter adjustment range. The magnetic field distribution state in the braiding environment is adjusted through a magnetic field control system. The magnetic field strength and direction parameters are corrected according to the parameter adjustment range to obtain the magnetic field control configuration. Based on the magnetic field control configuration, braiding process conditions, including braiding speed, tension control, and wire diameter arrangement angle, are adjusted to improve the spatial distribution state of the copper wires through process parameter linkage adjustment. A support vector machine algorithm is used to establish a correlation model between copper wire arrangement accuracy and shielding effectiveness. The effect of improved arrangement accuracy is predicted based on the adjusted process parameters, obtaining a quantitative evaluation result of accuracy optimization. A corrected process parameter combination is generated based on the accuracy optimization evaluation result. If the verification is successful, the process parameter configuration scheme is determined.

[0031] Specifically, in the real-time braiding quality evaluation system, when the electromagnetic shielding effectiveness detection value is below 85dB (standard threshold), a parameter adjustment mechanism is triggered. First, the periodic signal of the braided structure is analyzed using Fast Fourier Transform. When the amplitude of the spatial frequency spectrum decreases by 15% at a period of 1.2mm⁻¹, it is determined that the copper wire alignment is inaccurate. The system automatically calls a genetic algorithm for parameter optimization, using magnetic field strength (initial value 0.5T) and braiding angle (initial value 45 degrees) as variables, and setting the fitness function as a weighted sum of shielding effectiveness and energy consumption ratio (weighting coefficients 0.7:0.3). After 20 iterations, the optimal solution is obtained with a magnetic field strength of 0.63T±0.02T and a braiding angle of 48.5 degrees. At this point, finite element simulation shows that the standard deviation of the copper wire spacing decreases from 0.15mm in the original process to 0.08mm. After the process parameters were corrected, the system monitored the quality data for three production cycles in real time. Using the moving average method (window size of 5), the average shielding effectiveness was verified to have increased to 88.3 dB, and the process capability index CPK increased from 1.12 to 1.45. Finally, the optimized parameter combination was written into the process knowledge base, and a three-dimensional response surface model of magnetic field strength-weaving angle-shielding effectiveness was established for direct use by subsequent products of similar specifications.

[0032] S106. The braiding process is continuously monitored using the modified process parameter combination. The change trend of the copper wire arrangement is tracked through the real-time monitoring system. If the change trend deviates from the preset track, the intelligent braiding adjustment program is automatically started to obtain a stable and controllable braiding process state.

[0033] The system acquires copper wire position coordinate data and tension data from sensors in the braiding equipment. A data preprocessing module filters out noise from this data to obtain purified copper wire arrangement data. Based on this purified data, the position and angle offsets at adjacent time points are calculated. A sliding window method is used to analyze these offsets over a continuous time period to determine the trend vector of the copper wire arrangement change. If the amplitude of this trend vector exceeds a preset threshold, an abnormal deviation in the braiding process is identified, and a quantified deviation value is obtained. A support vector machine algorithm is used to analyze the mapping relationship between the quantified deviation value and historical adjustment parameters. The optimal adjustment strategy is matched based on the current deviation characteristics to obtain the process parameter correction amount. The output values ​​of the tension and speed controllers of the braiding equipment are adjusted using this correction amount to obtain the corrected braiding process state. Copper wire arrangement data is reacquired using this corrected braiding process state, and the state difference before and after correction is calculated. The preset track parameters and adjustment strategy weight coefficients are updated based on the state difference value to form an adaptive optimization monitoring and adjustment system.

[0034] Specifically, during the copper wire weaving process, the system first adopts a modified combination of process parameters, setting the weaving speed to 15.2 meters per minute, tension to 8.5 Newtons, and temperature to 45.3 degrees Celsius. The real-time monitoring system acquires 30 frames per second using a high-precision CCD camera, employs an improved Canny edge detection algorithm to identify copper wire boundaries, and calculates the standard deviation of the copper wire spacing to be 0.08 millimeters. The system establishes a copper wire arrangement status evaluation model, using a weighted average method to calculate a comprehensive score, with spacing uniformity weighted at 0.4, angle consistency at 0.3, and surface flatness at 0.3. The current comprehensive score is 92.6 points. The preset track range is set to 90 to 95 points. The system analyzes the trend of the most recent 50 data points using a sliding window algorithm, finding that the score shows a downward trend with a slope of -0.12 points per second. When the trend deviation exceeds the threshold of 2.5 points, the intelligent weaving adjustment program automatically starts, using a PID control algorithm to adjust the weaving parameters, with a proportional coefficient of 1.2, an integral coefficient of 0.8, and a derivative coefficient of 0.3. The system automatically adjusts the weaving speed to 14.8 meters per minute and increases the tension to 8.8 Newtons based on the deviation. After 15 seconds of adjustment, the overall score of the copper wire arrangement status rises to 93.4 points, and the system returns to a stable and controllable state. The entire closed-loop control process achieves automatic optimization of weaving quality.

[0035] S107. Establish a shielding effectiveness prediction model based on the braiding process status data. By analyzing the correlation between braiding density and shielding performance, if the predicted shielding effectiveness meets the design requirements, output a qualified vehicle cable shielding braiding product to obtain a high-quality cable that meets the anti-interference requirements of automotive electronic systems.

[0036] The process acquires state data during the braiding process, including braiding speed, tension parameters, and material density distribution information. This state data is collected in real time by sensors to obtain a process control dataset. Data preprocessing is performed on the process control dataset, using filtering algorithms to remove noise interference, marking outliers, and determining a standardized braiding density feature vector. A neural network prediction model is established using this feature vector, and the model is trained to learn the mapping relationship between braiding density and shielding effectiveness, obtaining a shielding performance prediction function. The shielding performance prediction function is used to evaluate the performance of the current braiding parameters, calculate the expected shielding effectiveness value, and obtain an anti-interference index. If the anti-interference index reaches a preset design standard threshold, the current braided product is deemed qualified, a quality certification mark is generated, and the cable quality level is determined. Based on the cable quality level, products are classified and labeled, and qualified braided products are automatically packaged to obtain the final cable product. The quality data of the final cable product is used to update the parameters of the neural network prediction model, and a feedback learning mechanism is used to optimize the accuracy of the neural network prediction model, resulting in an improved shielding effectiveness prediction system.

[0037] Specifically, establishing a shielding effectiveness prediction model first requires collecting key state data during the weaving process, including parameters such as a weaving density of 16 copper wires per square centimeter, a weaving angle of 45 degrees, a weaving tension of 2.5 Newtons, and a wire diameter of 0.15 mm. A prediction model is established using a multiple linear regression algorithm, where the relationship between shielding effectiveness SE and weaving density D, weaving angle θ, and wire diameter r is: SE = 85.2 + 12.5 × D - 0.8 × θ + 45.6 × r, with a correlation coefficient of 0.92. In actual predictions, when the weaving density is 18 wires per square centimeter, the predicted shielding effectiveness is calculated to be 98.4 dB. To verify the model's accuracy, a support vector machine algorithm is used for comparative verification. The radial basis function kernel parameter γ is set to 0.05, the penalty parameter C to 100, and the training sample includes 200 sets of historical data. Test results show that the prediction error is controlled within 3.2%. When the predicted shielding effectiveness exceeds the design threshold of 95 dB, the system automatically determines it as a qualified product and outputs it to the next process. The entire prediction process is sampled 10 times per second by a real-time data acquisition system to ensure dynamic monitoring during the weaving process. The final produced vehicle cables have a shielding effectiveness of over 96 dB in the 20 MHz to 1000 MHz frequency band, meeting the anti-interference requirements of automotive electronic systems in complex electromagnetic environments.

[0038] Based on the embodiments of the present invention described above, and through the above description, those skilled in the art can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for controlling the arrangement of copper wires during the braiding process of vehicle-mounted cables, characterized in that, The method includes the following steps: S101. Obtain real-time spatial state data of copper wires during the braiding process of vehicle-mounted cables. Collect three-dimensional coordinate information and motion trajectory parameters of copper wires in the braiding area through a high-precision sensor array. Establish a basic process condition database based on the material characteristics and wire diameter specifications of copper wires to obtain the initial feature vector of copper wire arrangement state. S102. The adaptive magnetic field adjustment algorithm is used to analyze the feature vector of the copper wire arrangement state. If the deviation of the copper wire spacing exceeds the preset threshold range, the magnetic field strength distribution parameters are dynamically adjusted. The copper wire is precisely positioned in the weaving process by combining multiple magnetic field generators to determine the optimal magnetic field control strategy. S103. Execute the copper wire arrangement optimization algorithm according to the magnetic field control strategy. By adjusting the magnetic field gradient distribution of each braiding point in real time, the copper wires are arranged in an orderly manner according to the predetermined braiding angle and density requirements, so as to obtain a uniform and stable braiding density distribution state. S104. The weaving density distribution is continuously evaluated by an optical detection system, and high-resolution image data of the surface of the weaving layer is collected. If a weaving angle deviation or density uneven area is detected, quality abnormality identification information is generated to obtain real-time evaluation results of the weaving quality. S105. Trigger the parameter adjustment mechanism based on the real-time evaluation result of the weaving quality. If the quality evaluation shows that the shielding effectiveness index is lower than the standard requirement, then backtrack and adjust the magnetic field control parameters and weaving process conditions. Optimize the spatial arrangement accuracy of the copper wires through closed-loop feedback control and determine the corrected combination of process parameters. S106. The braiding process is continuously monitored using the modified process parameter combination. The copper wire arrangement state change trend is tracked through the real-time monitoring system. If the change trend deviates from the preset track, the intelligent braiding adjustment program is automatically started to obtain a stable and controllable braiding process state. S107. Establish a shielding effectiveness prediction model based on the braiding process status data. By analyzing the correlation between braiding density and shielding performance, if the predicted shielding effectiveness meets the design requirements, output a qualified vehicle cable shielding braiding product to obtain a high-quality cable that meets the anti-interference requirements of automotive electronic systems.

2. The method for controlling the arrangement of copper wires during the braiding process of vehicle-mounted cables according to claim 1, characterized in that, S101 includes: The raw three-dimensional coordinate data of the copper wire collected by the sensor array is obtained, and noise interference is filtered out by the data preprocessing module to obtain the purified spatial position information. Based on the purified spatial location information, the motion trajectory parameters of the copper wire are calculated, and the velocity vector and acceleration vector of the copper wire in the weaving area are extracted by the trajectory analysis algorithm to determine the motion state characteristics of the copper wire. If the matching result between the copper wire motion state characteristics and the preset wire diameter specification parameters exceeds the preset threshold range, the trajectory correction mechanism is triggered to obtain standardized motion trajectory data. By combining the standardized motion trajectory data with the copper wire material properties, a multi-dimensional feature vector of the copper wire arrangement state is generated through a feature extraction algorithm, thus obtaining the state description matrix of the weaving process. Based on the distribution of eigenvectors in the state description matrix, calculate the distance deviation and angle deviation parameters between copper wires; If the distance deviation or angle deviation parameter exceeds the allowable range of the process parameters, the control system is invoked to send an adjustment command to the weaving equipment; The process parameter database is updated by updating the corrected copper wire arrangement state data, and the feature vector extraction model is optimized by incremental learning. The distance and angle deviation parameters between the copper wires are recalculated based on the updated process parameter database until the parameters meet the process requirements.

3. The method for controlling the arrangement of copper wires during the braiding process of vehicle-mounted cables according to claim 1, characterized in that, S102 includes: The position coordinate data during the copper wire weaving process is obtained, and the spatial distribution information of the copper wire arrangement is collected by an image sensor to obtain the original position data matrix; The spacing between adjacent copper wires is calculated based on the original position data matrix, and the spacing deviation is calculated using the Euclidean distance formula to obtain the spacing deviation distribution. The distribution of the spacing deviation is analyzed by the support vector machine algorithm, and the feature vector of the copper wire arrangement is extracted. If the deviation value in the feature vector exceeds the preset threshold range, the copper wire arrangement is judged to be abnormal, and the abnormal area identification information is obtained. The abnormal area identification information is used to drive the magnetic field strength adjustment module, and the magnetic field strength compensation value is calculated according to the degree of deviation to obtain the target magnetic field strength parameters; The target magnetic field strength parameters are received by a multi-point magnetic field generator group, and the copper wire trajectory is predicted by a Kalman filter algorithm. If the predicted trajectory deviates from the standard trajectory beyond the tolerance range, the magnetic field output power of each generator is adjusted in real time to determine the precise positioning control command. According to the precise positioning control command, multiple magnetic field generators are driven synchronously to form a spatial magnetic field distribution gradient through the magnetic field superposition effect, thereby obtaining the force balance state of the copper wire in the braiding area. A feedback control mechanism is used to monitor the deviation between the actual position and the target position of the copper wire. If the deviation continues to decrease and stabilizes within the preset range, the current magnetic field control strategy is determined to be the optimal configuration scheme.

4. A method for controlling the arrangement of copper wires during the braiding process of vehicle-mounted cables according to any one of claims 1-3, characterized in that, S103 includes: The spatial coordinates of the braiding points and the material properties of the copper wire are obtained. The magnetic field strength data of each braiding point are collected through a sensor array to obtain the initial magnetic field distribution matrix. The magnetic field gradient difference between each weaving point is calculated based on the initial magnetic field distribution matrix. The gradient distribution parameters are determined using the gradient calculation formula, where G represents the magnetic field gradient, B represents the magnetic field strength, and r represents the spatial distance, thus obtaining the gradient distribution feature vector. If there are weaving points in the gradient distribution feature vector where the gradient difference exceeds a preset threshold, the magnetic field control module is activated to adjust the magnetic field strength of the abnormal points. The local magnetic field distribution is changed by adjusting the coil current intensity to obtain the adjusted magnetic field distribution state. A genetic algorithm is used to optimize the adjusted magnetic field distribution state. The fitness function is the minimization of the weaving angle deviation. The magnetic field parameter configuration is optimized through selection, crossover and mutation operations to determine the optimal combination of magnetic field control parameters. The magnetic field generator at each braiding point is driven according to the optimal magnetic field control parameter combination to monitor the displacement trajectory and angle change of the copper wire under the action of the magnetic field in real time, and the actual arrangement state data of the copper wire is obtained through the position feedback sensor. If the actual arrangement data of the copper wires shows that the braiding density distribution is uneven, a PID control algorithm is used to adjust the magnetic field gradient distribution. The magnetic field control signal is corrected through proportional, integral and derivative elements to obtain a stable braiding density distribution result. The density detection module performs a quality assessment on the knitting density distribution results, calculates the density uniformity index and stability coefficient, and generates a knitting quality assessment report and optimization suggestion parameters.

5. A method for controlling the arrangement of copper wires during the braiding process of vehicle-mounted cables according to any one of claims 1-3, characterized in that, S104 includes: Image data of the braided layer surface is acquired, and the image data is acquired by an optical detection device, with the acquisition frequency dynamically adjusted according to the braiding speed; The image data is preprocessed; if the image quality is lower than a preset threshold, it is re-acquired to obtain a woven structure image. Fiber arrangement feature information is extracted from the braided structure image, and the feature information includes fiber direction and interlacing point position; The feature information is analyzed using an edge detection algorithm to determine the weaving angle parameters and density distribution data; A standard angle range model is established for the aforementioned weaving angle parameters. If the measured angle deviation exceeds the standard range, it is marked as an angle abnormality area. The density distribution data is analyzed using density statistics methods to calculate the degree of difference between local density values ​​and the overall average density. If the difference exceeds a preset tolerance range, it is identified as a region with uneven density. By using a convolutional neural network to comprehensively analyze regions with abnormal angles and uneven density, quality anomaly identification information is generated, and the knitting quality assessment results are obtained. A quality trend database is established based on the knitting quality assessment results to record the types and locations of anomalies, thus forming a knitting quality monitoring file.

6. A method for controlling the arrangement of copper wires during the braiding process of vehicle-mounted cables according to any one of claims 1-3, characterized in that, S105 includes: Real-time data on shielding effectiveness during the copper wire braiding process is obtained. Electromagnetic shielding strength, conductivity continuity, and braiding density parameters are collected through a sensor array to obtain the effectiveness index value of the current braiding state. The performance index values ​​are compared and analyzed according to the preset shielding effectiveness threshold standard. If the real-time performance index is lower than the standard threshold, a quality anomaly indicator is triggered. A parameter backtracking mechanism is used to analyze the process factors corresponding to the quality anomaly indicators. By analyzing historical data correlation, the degree of deviation of magnetic field strength, weaving tension and wire diameter spacing is identified, and the parameter adjustment range is determined. The magnetic field distribution in the weaving environment is adjusted by the magnetic field control system. The magnetic field strength and direction parameters are corrected according to the parameter adjustment range to obtain the magnetic field control configuration. Based on the magnetic field control configuration, the braiding process conditions are adjusted, including braiding speed, tension control and wire diameter arrangement angle, and the spatial distribution of copper wires is improved by linking and adjusting process parameters. A correlation model between copper wire arrangement accuracy and shielding effectiveness was established using the support vector machine algorithm. The effect of improving arrangement accuracy was predicted based on the adjusted process parameters, and a quantitative evaluation result of accuracy optimization was obtained. Based on the accuracy optimization evaluation results, a corrected combination of process parameters is generated. If the verification is successful, the process parameter configuration scheme is determined.

7. A method for controlling the arrangement of copper wires during the braiding process of vehicle-mounted cables according to any one of claims 1-3, characterized in that, S106 includes: The position coordinate data and tension data of copper wire collected by the sensors of the braiding equipment are acquired. The position coordinate data and tension data are then processed by the data preprocessing module to remove noise, so as to obtain the purified copper wire arrangement state data. Based on the purified copper wire arrangement data, calculate the position offset and angle offset at adjacent time points, and use the sliding window method to analyze the position offset and angle offset over a continuous time period to determine the trend vector of the copper wire arrangement. If the magnitude of the change trend vector exceeds the preset threshold range, it is determined that the weaving process has an abnormal deviation state, and the degree of deviation is quantified. The support vector machine algorithm is used to analyze the mapping relationship between the quantified deviation value and the historical adjustment parameters. Based on the current deviation characteristics, the optimal adjustment strategy is matched to obtain the process parameter correction amount. By adjusting the output values ​​of the tension controller and speed controller of the weaving equipment by the process parameter correction amount, the corrected weaving process state is obtained. The copper wire arrangement data was re-acquired using the corrected weaving process state, and the state difference value before and after the correction was calculated. The preset track parameters and adjustment strategy weight coefficients are updated based on the state difference values ​​to form an adaptive optimization monitoring and adjustment system.

8. A method for controlling the arrangement of copper wires during the braiding process of vehicle-mounted cables according to any one of claims 1-3, characterized in that, S107 includes: Acquire state data during the weaving process, including weaving speed, tension parameters, and material density distribution information. Collect the state data in real time through sensors to obtain a process control dataset. Data preprocessing is performed based on the process control dataset, noise interference is removed using a filtering algorithm, abnormal values ​​are marked, and a standardized weaving density feature vector is determined. A neural network prediction model is established using the braiding density feature vector. The neural network prediction model is trained to learn the mapping relationship between braiding density and shielding effectiveness, thereby obtaining a shielding performance prediction function. The shielding performance prediction function is used to evaluate the performance of the current weaving parameters, calculate the expected shielding effectiveness value, and obtain the anti-interference index. If the anti-interference index reaches the preset design standard threshold, the current braided product is judged to be qualified, a quality certification mark is generated, and the cable quality level is determined. The products are classified and marked according to the cable quality grade, and qualified braided products are automatically packaged to obtain the final cable products. The parameters of the neural network prediction model are updated by the quality data of the final cable product, and the accuracy of the neural network prediction model is optimized by a feedback learning mechanism, resulting in an improved shielding effectiveness prediction system.